Coder Legion - Recent posts tagged engineer

Electronics Engineer guide into Software Programming

Mon, 28 Sep 2026 09:48:17 +0000

As an electronics engineering student, I was super confident working with hardware building circuits, connecting sensors, and making things happen in the physical world. My project, a water quality sensing smartwatch, was moving along just fine until I hit the software part. All I knew was how to blink an LED or print a few lines on a screen.

I almost asked a software student to do it for me or considered switching to an easier project with code I could just copy. But deep down, I wanted to fully understand everything myself. That’s when I decided to take a full-stack software development class.

Looking back, full-stack wasn’t really the path that matched my actual reason for learning to code. My main goal was to build solid software for my hardware projects, not to develop entire web applications. Even though the journey was way bumpier than it needed to be, the experience turned out great, it gave me valuable coding skills I wouldn’t trade for anything.

Still, I can’t help but think: if I had the right guide from the start, my whole learning strategy could have been more focused, less confusing, and honestly, a lot smoother. That’s exactly why I’m writing this blog, for electronics engineers like you, who want to confidently build both hardware and software without wasting time figuring everything out the hard way. This is the step-by-step guide I wish I had.

Figure Out Why You Want to Learn Coding as an Electronics Engineer

The most important place to start isn’t picking a language or choosing a course, it’s figuring out why you want to move into software in the first place. Trust me, this step is what will make your learning path so much clearer.

Ask yourself:
👉 Are you completely new to coding and need to start from scratch?
👉 Do you want to level up your skills so you can confidently write full IoT project code from start to finish?
👉 Or maybe you just want to finally understand all those IoT code examples out there instead of blindly following them?
👉 Or are you actually considering a full career shift into software?

Be honest with yourself here, your reason matters. It shapes what you’ll focus on, the tools you’ll use, and the learning style that will work best for you.

For me, my main goal was to build software that could power my hardware projects. But I took a path meant for people switching fully into software development. It still turned out well, but knowing exactly why I was learning could have saved me time and helped me find a better starting point.

So before you dive into tutorials, bootcamps, or coding schools, take a beat to really understand your why. It’s what’s going to keep you focused, motivated, and moving in the right direction.

Choose the Right Coding Path for Electronics Engineers

Once you understand why you want to learn coding, the next step is to choose the right learning path based on your specific goals. This is where many electronics engineers, including myself, get misaligned. Selecting the wrong path can slow you down or lead you to spend time building skills that may not directly serve your primary objective.

This section provides a clear guide on where to start based on your goal, so you can begin with purpose and direction.

Best Path for Electronics Engineers Working with Arduino, Raspberry Pi, or ESP32
If your focus is on building IoT systems, working with microcontrollers, or programming devices like Arduino, Raspberry Pi, or ESP32, you need to build both low-level and high-level coding skills. These platforms are popular, accessible, and powerful tools for electronics engineers. I would recommend:

Start with Python Basics
Python is widely used with Raspberry Pi and is perfect for handling sensor data, device control, and cloud communication in IoT systems.
Learn C/C++ for Embedded Systems
C/C++ is the core language for programming Arduino, ESP32, and similar microcontrollers. It helps you control hardware at a low level, write efficient code, and build reliable embedded systems.
Work with IoT Boards Directly
Get hands-on experience with Arduino IDE, ESP-IDF for ESP32, or Raspberry Pi OS to write, upload, and test your code directly on the boards.
Understand Cloud Platforms (Firebase, AWS IoT)
Cloud integration allows your devices to store data remotely, communicate in real-time, and scale across networks.
Study Basic Databases (SQL or MongoDB)
Knowing how to manage and structure data is essential for building complete IoT applications that collect and analyze sensor data.
Why This Path Matters:
This is the most direct and effective path for electronics engineers who want to stay close to hardware while becoming fully capable of writing the software that powers their devices.
It gives you the foundation to create smart devices, connect them to the internet, and manage them through cloud platforms.

Over time, this path can prepare you for advanced fields like:

Machine learning on microcontrollers
Edge computing for real-time processing
Smart system automation
This is the coding roadmap that can truly empower you to design, build, and program your own IoT systems confidently.

Best Path for Electronics Engineers Specializing in Machine Learning and Data Science
If you are interested in designing smart systems that can collect, process, and make decisions based on data, specializing in Machine Learning (ML) and Data Science is a powerful choice. These skills are especially valuable when combined with your electronics background, as they can help you build intelligent IoT devices and smart automation systems.

Python for Data Science: Python is the standard language in this field.
Pandas and NumPy: Essential libraries for handling and analyzing large datasets.
Machine Learning Basics: Learn frameworks like TensorFlow or Scikit-Learn to build basic machine learning models.

Why This Path Matters:
This path equips you with the skills to design intelligent, connected systems that can adapt, learn, and improve over time.
As electronics engineers, moving into machine learning and data science allows you to:

Build smart IoT devices that can process and act on data independently.
Design predictive maintenance systems for electronics hardware.
Develop automation solutions that are not just programmed but can learn and optimize performance.
Machine learning brings a new layer of intelligence to your hardware projects. You won’t just be building devices that collect data, you’ll be building systems that can understand, predict, and solve real-world problems.

This learning path also sharpens your problem-solving skills and helps you grow as a versatile engineer who can work confidently across hardware, software, and data.
By choosing this route, you open doors to exciting fields like artificial intelligence, robotics, and smart system design, areas that are rapidly shaping the future of engineering.

Best Path for Electronics Engineers Transitioning fully to Software Development in 2025
If your primary interest is to shift from electronics to software engineering and build full web or mobile applications, a full-stack development path is what I would highly recommend.

Frontend Development: Learn HTML, CSS, JavaScript, and React to build user-facing applications with clean, interactive interfaces.
Backend Development: Learn Node.js, Python, and database management (SQL or MongoDB) to develop server-side logic and handle data storage.
Why This Path Matters:
This route opens up broad opportunities in web and mobile application development. It is ideal if you are planning to step away from hands-on hardware work and focus fully on software engineering.

Personal Note: This is the path I chose. While it greatly improved my coding skills, it was not fully aligned with my initial goal of coding for IoT. I recommend choosing this path only if you genuinely want to pursue a long-term career in software development.

Where to Start Learning Coding: Choose Your Guided Learning Journey
Now that you’ve found your why and picked your coding path, it’s time to figure out where you should actually begin learning. But here’s the thing: how you learn matters just as much as what you learn.

Not everyone learns the same way. Some people grow best when they’re figuring things out solo, others need structured support, and some are still exploring what fits.
This blog is here to guide you to the starting point that feels right for you: no guesswork, no overwhelm.
Let’s help you choose your personalized learning journey.

Option 1: You’re a Self-Learner
If you love exploring things on your own, moving at your own pace, and switching up your learning when curiosity hits, this is your vibe.

Check out the blog: Top 13 Websites to Learn Coding for Free in 2025
This blog will guide you to trusted platforms where you can start learning coding on your own, step-by-step, without pressure.

This works best if:

You’re balancing school, work, or other commitments.
You like controlling your own learning schedule.
You prefer discovering and experimenting as you go.
Even when you’re self-learning, don’t go at it totally alone. Connect with coding communities on Discord, Reddit, or LinkedIn. You’ll find support, answers, and people who get what you’re going through.

Option 2: You Learn Best with Structure
If you prefer having a teacher, classmates, and clear deadlines to keep you focused, structured learning might be your sweet spot.

Dive into the blog:13 Best Coding Schools in Kenya (2025 Edition)
This blog will guide you to schools that can offer mentorship, peer support, and the structure you need to grow fast and confidently.

This is perfect if:

You need accountability to stay consistent.
You like learning through collaboration and class discussions.
You’re energized by having classmates and real-time feedback.
Before choosing a school, reach out to past students to hear what the experience is really like, that’s where you’ll get the real, unfiltered feedback.

Option 3: You’re Still Exploring
If you’re just out of high school, thinking of switching careers, or still trying to figure out your best move, that’s totally valid. You don’t need to have all the answers yet.

Explore the blog: Coding Bootcamps vs University Degree vs Online Courses in 2025 — Choose the Best Option
This blog will walk you through the pros and cons of each path so you can make a smart, confident decision about your next step.

This is your go-to if:

You’re not sure whether to choose self-learning, coding schools, or university.
You want a breakdown of cost, time, and career opportunities for each option.
You need help comparing different learning journeys in one place.
It’s okay to mix learning styles. You can self-learn now and join a bootcamp later. Your journey doesn’t have to be one-size-fits-all.

Whether you choose to self-learn, join a coding school, go to university, or build your own custom mix, the most important thing is to start. You can figure things out along the way.

Learning to code as an electronics engineer will unlock powerful doors. You’ll gain the skills to design real solutions, confidently build hardware and software systems, and solve problems that matter in the real world.

Conclusion

Learning to code as an electronics engineer isn’t just a “nice-to-have” anymore, it’s your power move. Whether you’re building smart devices, diving into machine learning, or switching to full-stack development, coding is the tool that will unlock your next level.

The most important thing?
Start where you are.
Learn in a way that fits you.
Keep moving, even when it gets hard.

When I started, I was just trying to figure out how to program my water quality sensing smartwatch. I didn’t know much, but I kept learning, coding microcontrollers, working with sensors, and step by step, I got it done. Today, I’ve grown from an intern to a technical mentor, and I’m now teaching my first class while still learning new skills in DevOps. It’s been a journey, and I’ve never been happier.

That’s why I’m sharing this because I know what’s possible when you stick with it. Your journey doesn’t have to look like anyone else’s. You can self-learn, join coding schools, explore university, or mix it all up, and that’s perfectly okay. Progress isn’t always a straight line. Sometimes you’ll pivot, pause, or even feel lost. That’s part of the process. Trust me, you’ll find your rhythm.

If I could give you one piece of advice from my own story, it’s this: stay curious and be patient with yourself. The skills will come, the confidence will grow, and soon you’ll look back and realize you’ve built something incredible.

The future belongs to engineers who can confidently blend hardware and software and that can absolutely be you.

Electronics Engineer!! How did I end up as a technical mentor?

Mon, 21 Sep 2026 12:26:48 +0000

When I was studying Electronics Engineering, the natural next step after coursework was to secure an internship, complete my senior project, and transition into the professional space. But things did not unfold in a straight line. Getting an internship proved difficult, and rather than staying stuck, I decided to change the direction of my career entirely. I took a gap year and joined a full-stack software engineering program at Zindua School.

My motivation at the time was practical. I needed programming skills to complete my senior project: a water-quality sensing smartwatch. I did not plan to become a software engineer. I wanted to understand how to build the software component of my hardware idea. But that decision opened a door I didn’t know I would walk through.

For anyone coming from an electronics background and trying to figure out where to even begin with software, I shared my own starting point, challenges, and the transition process in another blog here: How Electronics Engineers Can Successfully Learn Coding in 2025 — New IoT & Software Guide (https://medium.com/@imorgansonia/how-electronics-engineers-can-successfully-learn-coding-in-2025-new-iot-software-guide-20bf1360a900).

During the program, I gained enough understanding to write code, build things, and experiment. I was still a beginner, but I was no longer lost. I could build, break, debug, and try again. After I completed the course, I returned to working on my project, and although I quickly realized I still had a lot to learn, I now had direction. The skills I had gained were a foundation, not a final product.

And then something unexpected happened.

Zindua School reached out. They were building a program to train alumni who had shown potential during their time as students, preparing them to become technical mentors. This was only three months after I had completed the program. The opportunity felt big, and honestly, I was not sure if I could do it. At the same time, I still needed to fulfill my university internship requirement. I reached out to my lecturer, explained the opportunity, and his response was simple:
“Is it going to be more than just training? Is there a promise of growth?”
I said yes. And that is how my mentorship journey began.

I spent my first month learning under Cecilia, a technical mentor who guided me not only on how to teach but also on balancing the multiple responsibilities that came with the role. She allowed me to facilitate small sessions, provided structured feedback, and ensured I understood why specific approaches matter, not just how to do them.

Eventually, I was given my own class of sixteen students. It was the largest cohort the school had enrolled at once, and being entrusted with it felt both an honor and an overwhelming responsibility. This is where the imposter syndrome surfaced. Teaching requires more than understanding the content. I had to prepare lessons, review 16 ongoing projects, and ensure that no student was falling behind. The pressure was real.

One training session with Osotwa completely shifted my perspective. We learned that it is okay and even crucial for students to see their mentor make mistakes in real time. When something breaks during live coding, or when a question arises that you are not entirely sure about, that moment is not failure. It is teaching. It shows students that even experienced developers debug, rethink, and adapt. What matters is how you rise from the problem, not avoiding the problem itself.

That realization gave me confidence. I led the class to the end of the program, and together we learned, built, struggled, and improved.

Growth did not stop there. I began intentionally reflecting on my teaching process, what worked, what needed improvement, and how I could ensure every new class received a better, more refined version of myself. Teaching is not just about explaining concepts; it is also about listening, adapting, and continually learning.

Beyond teaching, I wanted to build community. Zindua brings in new students every few weeks, and many of the classes do not cross paths. I saw an opportunity to create a connection, so I organized an internal hackathon. The goal was simple: bring students together, break routine, and give them a space to collaborate, experiment, and meet each other beyond their modules.

The hackathon achieved exactly that. Students from software and data science worked together, shared ideas, and tackled real challenges around education and student life. They presented creative solutions, including gamified learning systems that rewarded students with redeemable points for engagement. It was inspiring to watch, and also a learning experience for how to structure future events even better.

This entire journey has been shaped by stepping into opportunities before I felt “ready,” choosing to learn in public, and understanding that growth happens in cycles. Every class I mentor shapes me just as much as I shape them.

I am still learning, still building, and still becoming.
And I am grateful.

Here is to growing as a technical mentor, and to all the students who trust me to guide them along the way.

An Engineer's Productivity Stack in 2026: The Tools I Use Every Day

Sat, 01 Aug 2026 19:09:44 +0000

After more than 60 articles, 9 technical series, 4 production applications, and a bilingual blog that generates weekly content, I get asked the same question over and over: "What tools do you use?" Not what tools I recommend, not what tools are trending, but which ones I actually use, every single day, to build what you see on Datalaria.

This article is the answer. No sponsorships, no affiliate links, no filters. Every tool listed here has been tested in production, paid for (or not) with my own money, and documented in at least one post on this blog. If I haven't used it in a real project, it's not on this list.

The Workflow: 10 Phases, 10 Tools

The key to my productivity isn't in individual tools but in how they fit together. Each workflow phase feeds the next, and the output of one tool is the input of another. No silos; it's a pipeline.

Phase	Tool	Why this one
🧠 Think	Gemini Deep Research	Exhaustive research in minutes
✍️ Write	Hugo + VS Code + Markdown	Full control, Git-native, speed
💻 Code	Python + Pandas + FastAPI	The data engineering trident
🗄️ Store	Supabase (PostgreSQL)	Free BaaS, RLS, automatic REST APIs
🤖 Orchestrate AI	CrewAI + Gemini 2.5	Autonomous agents with Tool Calling
⚙️ Automate	GitHub Actions	Free CI/CD, event-driven
🚀 Deploy	Netlify	Deploy from Git in seconds
📧 Communicate	Brevo (Newsletter)	Free email marketing, API, segmentation
📊 Visualize	Chart.js + Vanilla JS	Lightweight, no heavy frameworks, interactive
📚 Learn	NotebookLM	Transforms any source into study resources

🧠 Think: Gemini Deep Research

Before writing a single line, I research. And this is where generative AI has radically changed my workflow. Gemini Deep Research (within Gemini Advanced) is the tool I use for exhaustive research before every article and every technical project.

When I was preparing the article on Thomas Bayes, I needed to verify dates, publications, historical context of the Royal Society, and the precise mathematical connection between Bayes' theorem and Facebook Prophet. What previously would have required hours of browsing through Wikipedia, Stanford Encyclopedia of Philosophy, and academic papers, Gemini Deep Research compiled into a structured report in under 10 minutes, with verifiable citations and sources.

The key: I don't use it to write; I use it to research. The final text is always mine. Gemini gives me the raw material; I build the narrative. I documented this approach in detail in AI in Education with Deep Research.

✍️ Write: Hugo + VS Code + Markdown

Everything you see on Datalaria is written in pure Markdown, edited in VS Code, compiled with Hugo, and versioned in Git. Zero WordPress, zero visual CMS, zero drag-and-drop.

Why this seemingly masochistic decision? Because a Hugo blog is code. I can run git diff to see what I changed in an article. I can run git blame to know when I changed it. I can fork, create a branch, experiment with a new structure, and merge only if it works. And I can automate deployment with a git push. I documented all these architectural decisions in Building Datalaria.

Hugo compiles the 700+ pages of this blog (Spanish + English) in under 4 minutes. A traditional CMS would take several seconds just to render a single page. When you iterate fast, compilation speed isn't a luxury; it's a necessity.

💻 Code: Python + Pandas + FastAPI

The trident I use for absolutely everything involving data:

Python as the base language. No debate. The library ecosystem for data engineering, ML, and automation has no rival.
Pandas for data manipulation, cleaning, and transformation. Every pipeline in the S&OP series — from sales data ingestion to forecast generation with Prophet — goes through Pandas.
FastAPI when I need to expose a service as a REST API. We used it in Part 6 of the Observability series to build the obsolescence radar backend, and in the OpenWeather app as a weather prediction backend.

🗄️ Store: Supabase (PostgreSQL)

Supabase is managed PostgreSQL with superpowers: authentication, Row Level Security (RLS), automatic REST APIs generated from the database schema, and a free tier that covers 95% of my development and prototyping needs.

I use it as the data backend in the Observability series (storing the component catalog, obsolescence alerts, and BOM graphs), in the S&OP pipelines (demand data, forecasts, production plans), and in the Snake game with a global leaderboard.

Why Supabase over Firebase? Because Supabase is real PostgreSQL. I can write native SQL, create materialized views, use complex JOINs, and migrate to any other managed PostgreSQL (RDS, Cloud SQL) without changing a single line of code. Firebase traps you in its proprietary ecosystem; Supabase includes the exit door.

🤖 Orchestrate AI: CrewAI + Gemini 2.5

When I need AI to not just answer questions but execute complex multi-step tasks, I use CrewAI as the agent orchestration framework. Each agent has a role, an objective, specific Python tools (decorated with @tool), and the ability to coordinate with other agents.

Gemini 2.5 Pro/Flash is the LLM powering the agents. The CrewAI + Gemini + Tool Calling combination is the architecture documented across the entire 9-part Autopilot series, from the automatic content generator to the Ops Copilot.

As we analyzed in the RAG vs. Tool Calling article, the key is separating the "semantic brain" (the LLM understands context) from the "deterministic muscle" (Python tools execute precision operations). The LLM thinks; the tools do.

⚙️ Automate: GitHub Actions

Every CI/CD pipeline at Datalaria runs on GitHub Actions. It's free for public repositories, event-driven (triggers on push, cron, webhook), and flexible enough to orchestrate everything from Hugo compilation to CrewAI pipeline execution.

In Autopilot Part 5, we documented how to configure a GitHub Actions workflow that runs the complete agentic pipeline every week: generates content with CrewAI, creates Markdown files, commits, pushes, and automatically deploys to Netlify. All without human intervention.

🚀 Deploy: Netlify

Netlify deploys Datalaria directly from the GitHub repository. Every git push to the main branch triggers a Hugo build and publishes the site in seconds. Features like Netlify Functions (serverless), redirects, and custom headers cover everything I need without managing servers.

We documented it in OpenWeather app Part 2 as the deployment platform for frontend applications with serverless backends.

Datalaria's newsletter uses Brevo (formerly Sendinblue). Generous free tier (300 emails/day), REST API for automation, audience segmentation, and a template editor.

In Autopilot Part 6, we documented how the CrewAI pipeline generates email content, builds the HTML, and sends it automatically via Brevo's API — closing the complete generation → publication → distribution cycle without manual intervention.

📊 Visualize: Chart.js + Vanilla JS

When I need interactive charts in web apps, I use Chart.js with vanilla JavaScript. No React, no Vue, no heavy frameworks. The philosophy is intentional: every library you add is a dependency to maintain, an attack surface to protect, and a bundle to inflate.

OpenWeather app Part 4 and Basic Visualizations demonstrate that Chart.js + pure CSS produces professional-quality interactive dashboards without needing a 200KB framework.

📚 Learn: NotebookLM

Google's NotebookLM is my accelerated learning tool. I upload technical documentation, academic papers, or conference transcripts, and NotebookLM generates summaries, study questions, and — most transformatively — audio podcasts where two hosts discuss the material as if it were a natural conversation.

I documented it in depth in NotebookLM + SQL, showing how to transform PostgreSQL documentation into interactive study resources.

What I Tried and Discarded

Not everything you try survives contact with production. These are the tools I evaluated and discarded, with reasons:

WordPress: Used it for years. Abandoned it for the slowness, the plugins, the constant security updates, and the impossibility of versioning content with Git. Hugo is 100x faster and everything is code.
LangChain: LLM orchestration framework I tried before CrewAI. Too much abstraction, deep inheritance chains that were hard to debug, and an API that changed with every minor version. CrewAI is simpler, more explicit, and more stable.
Streamlit: Excellent for rapid data dashboard prototypes. But when you need control over the frontend (CSS, animations, UX), Streamlit becomes a straitjacket. For production, I prefer FastAPI + HTML/CSS/JS, where I have full control.
MongoDB: Tried it as a PostgreSQL alternative for semi-structured data. But the lack of JOINs and the impossibility of complex relational queries quickly ruled it out for my industrial use cases (BOM graphs, demand table cross-referencing).

Total Stack Cost: The Real Economics

This is where the Hidden Economics of AI becomes directly relevant. How much does operating this entire stack cost?

Tool	Monthly cost	Notes
Gemini Advanced	~€22/month	Includes Deep Research, 2.5 Pro, etc.
Hugo	€0	Open source
VS Code	€0	Open source
Python + libraries	€0	Open source
Supabase	€0	Free tier
CrewAI	€0	Open source
GitHub Actions	€0	Free for public repos
Netlify	€0	Free tier
Brevo	€0	Free tier (300 emails/day)
Chart.js	€0	Open source
NotebookLM	€0	Included in Gemini Advanced
TOTAL	~€22/month

Virtually the entire stack is free or open source. The only recurring cost is the Gemini Advanced subscription, which covers both Deep Research and the models powering CrewAI agents. Even if we count Gemini API costs for Autopilot pipeline executions, the total rarely exceeds €5 extra per month.

Compare this with the cost of an "enterprise" stack: a licensed CMS (€50-500/month), a premium email marketing tool (€30-200/month), a managed hosting service (€20-100/month), and a BI platform (€50-300/month). The open source stack isn't just cheaper; it's more powerful, because every tool is a box you can open, inspect, modify, and learn from.

The Philosophy: Learning > Comfort

If there's a common thread across every decision in this stack, it's this: I prioritize learning over comfort. Hugo is harder than WordPress, but I learned static site generation, Go templates, and CI/CD. FastAPI is more work than Streamlit, but I learned REST API design, async/await, and OpenAPI. Supabase with native SQL is more verbose than Firebase, but I learned real PostgreSQL, RLS, and migrations.

Every tool in the stack isn't just a tool; it's a course. And the 60+ articles on this blog are the notes from those courses, shared openly so anyone can walk the same path.

As Deming would say: "Learning is not compulsory. Neither is survival." In a world where AI redefines the rules of the game every quarter, the stack you use matters less than your ability to learn the next stack. And that ability is built by choosing tools that force you to understand what's under the hood.